{
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  {
   "cell_type": "markdown",
   "id": "c3bcb06f",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# The Image Classification Dataset\n",
    "\n",
    "The MNIST dataset is one of the widely used dataset for image classification, while it is too simple as a benchmark dataset. We will use the similar, but more complex Fashion-MNIST dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "270279ba",
   "metadata": {
    "execution": {
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    },
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    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import time\n",
    "import torch\n",
    "import torchvision\n",
    "from torchvision import transforms\n",
    "from d2l import torch as d2l\n",
    "\n",
    "d2l.use_svg_display()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd71263f",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Download and read it into memory using built-in framework utilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9702ba11",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:35:56.979230Z",
     "iopub.status.busy": "2023-08-18T19:35:56.978838Z",
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     "shell.execute_reply": "2023-08-18T19:35:57.111496Z"
    },
    "origin_pos": 11,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 10000)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class FashionMNIST(d2l.DataModule):  \n",
    "    \"\"\"The Fashion-MNIST dataset.\"\"\"\n",
    "    def __init__(self, batch_size=64, resize=(28, 28)):\n",
    "        super().__init__()\n",
    "        self.save_hyperparameters()\n",
    "        trans = transforms.Compose([transforms.Resize(resize),\n",
    "                                    transforms.ToTensor()])\n",
    "        self.train = torchvision.datasets.FashionMNIST(\n",
    "            root=self.root, train=True, transform=trans, download=True)\n",
    "        self.val = torchvision.datasets.FashionMNIST(\n",
    "            root=self.root, train=False, transform=trans, download=True)\n",
    "\n",
    "data = FashionMNIST(resize=(32, 32))\n",
    "len(data.train), len(data.val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b31548fa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:35:57.116730Z",
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     "shell.execute_reply": "2023-08-18T19:35:57.127453Z"
    },
    "origin_pos": 14,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 32, 32])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.train[0][0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acb66d51",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Two utility functions to visualize the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ca95ebc5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:35:57.137128Z",
     "iopub.status.busy": "2023-08-18T19:35:57.136465Z",
     "iopub.status.idle": "2023-08-18T19:35:57.142322Z",
     "shell.execute_reply": "2023-08-18T19:35:57.141204Z"
    },
    "origin_pos": 16,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "@d2l.add_to_class(FashionMNIST)  \n",
    "def text_labels(self, indices):\n",
    "    \"\"\"Return text labels.\"\"\"\n",
    "    labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat',\n",
    "              'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']\n",
    "    return [labels[int(i)] for i in indices]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f16080d",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Reads a minibatch of data with size `batch_size`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "81f8afca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:35:57.160131Z",
     "iopub.status.busy": "2023-08-18T19:35:57.159304Z",
     "iopub.status.idle": "2023-08-18T19:35:57.397652Z",
     "shell.execute_reply": "2023-08-18T19:35:57.396242Z"
    },
    "origin_pos": 22,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64, 1, 32, 32]) torch.float32 torch.Size([64]) torch.int64\n"
     ]
    }
   ],
   "source": [
    "@d2l.add_to_class(FashionMNIST)  \n",
    "def get_dataloader(self, train):\n",
    "    data = self.train if train else self.val\n",
    "    return torch.utils.data.DataLoader(data, self.batch_size, shuffle=train,\n",
    "                                       num_workers=self.num_workers)\n",
    "\n",
    "X, y = next(iter(data.train_dataloader()))\n",
    "print(X.shape, X.dtype, y.shape, y.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "47e90ba5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:35:57.402928Z",
     "iopub.status.busy": "2023-08-18T19:35:57.402306Z",
     "iopub.status.idle": "2023-08-18T19:36:02.097749Z",
     "shell.execute_reply": "2023-08-18T19:36:02.096784Z"
    },
    "origin_pos": 24,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4.69 sec'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tic = time.time()\n",
    "for X, y in data.train_dataloader():\n",
    "    continue\n",
    "f'{time.time() - tic:.2f} sec'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "06fb4e72",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:36:02.101877Z",
     "iopub.status.busy": "2023-08-18T19:36:02.101254Z",
     "iopub.status.idle": "2023-08-18T19:36:02.105863Z",
     "shell.execute_reply": "2023-08-18T19:36:02.105019Z"
    },
    "origin_pos": 26,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "def show_images(imgs, num_rows, num_cols, titles=None, scale=1.5):  \n",
    "    \"\"\"Plot a list of images.\"\"\"\n",
    "    raise NotImplementedError"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "283bd053",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The images and their corresponding labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b16ddca3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:36:02.109988Z",
     "iopub.status.busy": "2023-08-18T19:36:02.109439Z",
     "iopub.status.idle": "2023-08-18T19:36:02.819862Z",
     "shell.execute_reply": "2023-08-18T19:36:02.811142Z"
    },
    "origin_pos": 28,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
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      ],
      "text/plain": [
       "<Figure size 1200x150 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@d2l.add_to_class(FashionMNIST)  \n",
    "def visualize(self, batch, nrows=1, ncols=8, labels=[]):\n",
    "    X, y = batch\n",
    "    if not labels:\n",
    "        labels = self.text_labels(y)\n",
    "    d2l.show_images(X.squeeze(1), nrows, ncols, titles=labels)\n",
    "batch = next(iter(data.val_dataloader()))\n",
    "data.visualize(batch)"
   ]
  }
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